In [11]: from astropy.stats.sigma_clipping import sigma_clip
    ...: from astropy.utils.masked import Masked
    ...: from astropy import units as u
    ...: 
    ...: # MaskedNDArray: the mask is ignored by sigma_clip()
    ...: flux = Masked([1., 2., 300.], mask=[False, True, True])

In [12]: sigma_clip(flux)
Out[12]: MaskedNDArray([  1.,   2., 300.])

# np.ma.MaskedArray is handled properly
In [13]: sigma_clip(np.ma.MaskedArray(flux))
Out[13]: 
masked_array(data=[1.0, --, --],
             mask=[False,  True,  True],
       fill_value=1e+20)

from astropy.stats.sigma_clipping import sigma_clip
from astropy.utils.masked import Masked
from astropy import units as u

# MaskedNDArray: it works
flux = Masked([1., 2., 3.], mask=[False, False, False])
clipped = sigma_clip(data=flux, sigma=5)

# MaskedQuantity: it fails with ValueError
flux_q = flux * u.percent
clipped_q = sigma_clip(data=flux_q, sigma=5)

ValueError                                Traceback (most recent call last)
Input In [626], in <cell line: 12>()
     10 # MaskedQuantity: it fails with ValueError
     11 flux_q = flux * u.percent
---> 12 clipped_q = sigma_clip(data=flux_q, sigma=5)
     13 print(clipped_q)

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\stats\sigma_clipping.py:835, in sigma_clip(data, sigma, sigma_lower, sigma_upper, maxiters, cenfunc, stdfunc, axis, masked, return_bounds, copy, grow)
    650 """
    651 Perform sigma-clipping on the provided data.
    652 
   (...)
    829 standard deviation is higher.
    830 """
    831 sigclip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower,
    832                     sigma_upper=sigma_upper, maxiters=maxiters,
    833                     cenfunc=cenfunc, stdfunc=stdfunc, grow=grow)
--> 835 return sigclip(data, axis=axis, masked=masked,
    836                return_bounds=return_bounds, copy=copy)

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\stats\sigma_clipping.py:638, in SigmaClip.__call__(self, data, axis, masked, return_bounds, copy)
    633 # These two cases are treated separately because when
    634 # ``axis=None`` we can simply remove clipped values from the
    635 # array. This is not possible when ``axis`` or ``grow`` is
    636 # specified.
    637 if axis is None and not self.grow:
--> 638     return self._sigmaclip_noaxis(data, masked=masked,
    639                                   return_bounds=return_bounds,
    640                                   copy=copy)
    641 else:
    642     return self._sigmaclip_withaxis(data, axis=axis, masked=masked,
    643                                     return_bounds=return_bounds,
    644                                     copy=copy)

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\stats\sigma_clipping.py:420, in SigmaClip._sigmaclip_noaxis(self, data, masked, return_bounds, copy)
    418 iteration += 1
    419 size = filtered_data.size
--> 420 self._compute_bounds(filtered_data, axis=None)
    421 filtered_data = filtered_data[
    422     (filtered_data >= self._min_value)
    423     & (filtered_data <= self._max_value)]
    424 nchanged = size - filtered_data.size

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\stats\sigma_clipping.py:302, in SigmaClip._compute_bounds(self, data, axis)
    300 with warnings.catch_warnings():
    301     warnings.simplefilter("ignore", category=RuntimeWarning)
--> 302     self._max_value = self._cenfunc_parsed(data, axis=axis)
    303     std = self._stdfunc_parsed(data, axis=axis)
    304     self._min_value = self._max_value - (std * self.sigma_lower)

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\stats\sigma_clipping.py:69, in _nanmedian(array, axis)
     66     axis = 0
     68 if isinstance(array, Quantity):
---> 69     return array.__array_wrap__(bottleneck.nanmedian(array, axis=axis))
     70 else:
     71     return bottleneck.nanmedian(array, axis=axis)

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\units\quantity.py:562, in Quantity.__array_wrap__(self, obj, context)
    557 def __array_wrap__(self, obj, context=None):
    559     if context is None:
    560         # Methods like .squeeze() created a new `ndarray` and then call
    561         # __array_wrap__ to turn the array into self's subclass.
--> 562         return self._new_view(obj)
    564     raise NotImplementedError('__array_wrap__ should not be used '
    565                               'with a context any more since all use '
    566                               'should go through array_function. '
    567                               'Please raise an issue on '
    568                               'https://github.com/astropy/astropy')

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\units\quantity.py:745, in Quantity._new_view(self, obj, unit)
    743 view = obj.view(quantity_subclass)
    744 view._set_unit(unit)
--> 745 view.__array_finalize__(self)
    746 return view

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\units\quantity.py:539, in Quantity.__array_finalize__(self, obj)
    537 super_array_finalize = super().__array_finalize__
    538 if super_array_finalize is not None:
--> 539     super_array_finalize(obj)
    541 # If we're a new object or viewing an ndarray, nothing has to be done.
    542 if obj is None or obj.__class__ is np.ndarray:

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\utils\masked\core.py:572, in MaskedNDArray.__array_finalize__(self, obj)
    567     super_array_finalize(obj)
    569 if self._mask is None:
    570     # Got here after, e.g., a view of another masked class.
    571     # Get its mask, or initialize ours.
--> 572     self._set_mask(getattr(obj, '_mask', False))
    574 if 'info' in obj.__dict__:
    575     self.info = obj.info

File C:\pkg\_winNonPortables\Anaconda3\envs\lkv2_1_dev\lib\site-packages\astropy\utils\masked\core.py:228, in Masked._set_mask(self, mask, copy)
    225 if ma.shape != self.shape:
    226     # This will fail (correctly) if not broadcastable.
    227     self._mask = np.empty(self.shape, dtype=mask_dtype)
--> 228     self._mask[...] = ma
    229 elif ma is mask:
    230     # Even if not copying use a view so that shape setting
    231     # does not propagate.
    232     self._mask = mask.copy() if copy else mask.view()

ValueError: could not broadcast input array from shape (3,) into shape ()